We expose our models as JSON-accessible web service APIs, as well as via R packages

Data Mining Analytics for Agriculture

DMINE labs is a grouping of researchers and students that use data mining and machine learning techniques to explore relationships of agriculture and climate. As part of our team work, we provide linux-based applications and analysis development server access, which we are using to create our data mining and machine learning processes.

Institutions supporting this research

DMINE is developed as part of collaboration with scientists @ the University of Idaho and Oregon State University. DMINE is funded thru the National Oceanic and Atmospheric Association (NOAA), under NOAA grant # NA15OAR4310145

University of Idaho

National Oceanic and Atmospheric Association

Oregon State University

University of Washington

Agricultural Data Mining Use Case Examples

Agricultural Managers and Farmers

Crop Commodity Analysis

Our agricultural analysis approach explores crop insurance claim data (loss and claim frequency), in comparison to bioclimatic variables, over a broad set of commodities and damage causes. The USDA’s agricultural commodity loss archive (1980-present) is used as a key data source.

Insurance Organizations

Predicting Commodity Loss

There is a known relationship between changing climate conditions and human health. Our work in this area uses known climatological variables, in concert with other associated feature variables, to construct a climate health predictive model.

Scientists and Researchers

Climate Impacts on Agriculture

Physical infrastructure is a huge expenditure in many aspects of society. Given these costs, impacts given climate change can have far reaching effects. Our work proposes to predict infrastructure resilience under changing climate regimes.